




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def        a__					(							snake_case__							)		->   Union[str, Any]:
			lowerCamelCase      =   [
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(snake_case__			,					snake_case__							)



def        a__					(							snake_case__							)		->   List[str]:
			lowerCamelCase ,  lowerCamelCase      =   emb.weight.shape
			lowerCamelCase      =   nn.Linear(snake_case__			,					snake_case__			,					bias=snake_case__							)
			lowerCamelCase      =   emb.weight.data
			return lin_layer



def        a__					(							snake_case__							)		->   List[Any]:
			lowerCamelCase      =   torch.load(snake_case__			,					map_location="""cpu"""							)
			lowerCamelCase      =   Namespace(**checkpoint["""cfg"""]["""model"""]							)
			lowerCamelCase      =   checkpoint["""model"""]
			remove_ignore_keys_(snake_case__							)
			lowerCamelCase      =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

			lowerCamelCase      =   {key.replace("""decoder"""			,					"""model"""							): val for key, val in state_dict.items()}

			lowerCamelCase      =   XGLMConfig(
			    vocab_size=snake_case__			,					max_position_embeddings=args.max_target_positions			,					num_layers=args.decoder_layers			,					attention_heads=args.decoder_attention_heads			,					ffn_dim=args.decoder_ffn_embed_dim			,					d_model=args.decoder_embed_dim			,					layerdrop=args.decoder_layerdrop			,					dropout=args.dropout			,					attention_dropout=args.attention_dropout			,					activation_dropout=args.activation_dropout			,					activation_function="""gelu"""			,					scale_embedding=not args.no_scale_embedding			,					tie_word_embeddings=args.share_decoder_input_output_embed			,					)

			lowerCamelCase      =   XGLMForCausalLM(snake_case__							)
			lowerCamelCase      =   model.load_state_dict(snake_case__			,					strict=snake_case__							)
			print(snake_case__							)
			lowerCamelCase      =   make_linear_from_emb(model.model.embed_tokens							)

			return model


if __name__ == "__main__":
					lowerCAmelCase				:     Tuple					=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
					parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					lowerCAmelCase				:     List[Any]					=				parser.parse_args()
					lowerCAmelCase				:     List[Any]					=				convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class        __magic_name__				(			unittest.TestCase		,   UpperCAmelCase__							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   load_tool("""text-classification"""					)
							self.tool.setup()
							lowerCamelCase      =   load_tool("""text-classification"""						,					remote=_a					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.tool("""That's quite cool"""						,					["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.remote_tool("""That's quite cool"""						,					["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.tool(text="""That's quite cool"""						,					labels=["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.remote_tool(text="""That's quite cool"""						,					labels=["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)



